Improving MRSA control through simulation and surveillance BACKGROUND: The infection control community is highly divided on the most appropriate strategy to control transmission of methicilllin-resistant Staphylococcus aureus (MRSA) in hospitals. One approach used at VA hospitals is to perform surveillance testing on all patients to identify and isolate MRSA carriers. An alternative approach that is more efficient and/or cost-beneficial would be of tremendous value to the VA. Our previous work suggests that an approach using targeted surveillance of patients classified as high risk for MRSA carriage using electronic data may be a more practical alternative consideration for an approach to MRSA surveillance. Given that large studies of infection control strategies are often prohibitively difficult and expensive, however, an alternative approach using detailed computer simulations might provide important insight and direction for future research efforts. OBJECTIVES: Control of MRSA transmission is dependent on the interactions of innumerable factors and processes, although relatively little is known about these interactions or the relative effectiveness of different MRSA control strategies. With this in mind, our objectives are to (a) develop an electronic classification algorithm to identify VA patients at high risk of MRSA carriage at the time of hospital admission; (b) adapt, calibrate, and validate an agent-based computer simulation of MRSA transmission to the VA inpatient setting; (c) use this computer simulation to gain a deeper understanding of the factors that influence MRSA transmission and control in hospitals; and (d) evaluate and compare alternative policies for MRSA control in VA hospitals, including targeted surveillance, with a particular focus on assessing the cost-benefit of these different strategies. METHODS: First we will create and validate an electronic classification rule to estimate risk of MRSA carriage in hospitalized veterans at the time of admission. Predictors of MRSA carriage in this population will be determined through a retrospective cohort study performed using electronic data from our collaborating VA sites. We will then gather local patient- and facility-level data from our collaborating VA sites to adapt our simulation to the VA setting and to create a base-case simulation scenario. Following validation of the model, we will then assess the various strategies and factors that impact MRSA transmission through simulation experiments. A targeted active surveillance strategy will be included by incorporating the performance of our classification rule into the simulation. Traditional quantitative epidemiologic methods will be used to analyze simulation results, with a focus on MRSA prevalence and transmission rates as outcomes. Cost-benefit analyses will also be performed using simulated cost figures.